.install_pkg |
Installs Julia packages if needed |
.julia_project_status |
Obtain the status of the current Julia project |
.set_seed |
Set a seed both in Julia and R |
.using |
Loads Julia packages |
BayesFluxR_setup |
Set up of the Julia environment needed for BayesFlux |
bayes_by_backprop |
Use Bayes By Backprop to find Variational Approximation to BNN. |
BNN |
Create a Bayesian Neural Network |
BNN.totparams |
Obtain the total parameters of the BNN |
Chain |
Chain various layers together to form a network |
Dense |
Create a Dense layer with 'in_size' inputs and 'out_size' outputs using 'act' activation function |
find_mode |
Find the MAP of a BNN using SGD |
Gamma |
Create a Gamma Prior |
get_random_symbol |
Creates a random string that is used as variable in julia |
initialise.allsame |
Initialises all parameters of the network, all hyper parameters of the prior and all additional parameters of the likelihood by drawing random values from 'dist'. |
InverseGamma |
Create an Inverse-Gamma Prior |
likelihood.feedforward_normal |
Use a Normal likelihood for a Feedforward network |
likelihood.feedforward_tdist |
Use a t-Distribution likelihood for a Feedforward network |
likelihood.seqtoone_normal |
Use a Normal likelihood for a seq-to-one recurrent network |
likelihood.seqtoone_tdist |
Use a T-likelihood for a seq-to-one recurrent network. |
LSTM |
Create an LSTM layer with 'in_size' input size, and 'out_size' hidden state size |
madapter.DiagCov |
Use the diagonal of sample covariance matrix as inverse mass matrix. |
madapter.FixedMassMatrix |
Use a fixed mass matrix |
madapter.FullCov |
Use the full covariance matrix as inverse mass matrix |
madapter.RMSProp |
Use RMSProp to adapt the inverse mass matrix. |
mcmc |
Sample from a BNN using MCMC |
Normal |
Create a Normal Prior |
opt.ADAM |
ADAM optimiser |
opt.Descent |
Standard gradient descent |
opt.RMSProp |
RMSProp optimiser |
posterior_predictive |
Draw from the posterior predictive distribution |
prior.gaussian |
Use an isotropic Gaussian prior |
prior.mixturescale |
Scale Mixture of Gaussian Prior |
prior_predictive |
Sample from the prior predictive of a Bayesian Neural Network |
RNN |
Create a RNN layer with 'in_size' input, 'out_size' hidden state and 'act' activation function |
sadapter.Const |
Use a constant stepsize in mcmc |
sadapter.DualAverage |
Use Dual Averaging like in STAN to tune stepsize |
sampler.AdaptiveMH |
Adaptive Metropolis Hastings as introduced in |
sampler.GGMC |
Gradient Guided Monte Carlo |
sampler.HMC |
Standard Hamiltonian Monte Carlo (Hybrid Monte Carlo). |
sampler.SGLD |
Stochastic Gradient Langevin Dynamics as proposed in Welling, M., & Teh, Y. W. (n.d.). Bayesian Learning via Stochastic Gradient Langevin Dynamics. 8. |
sampler.SGNHTS |
Stochastic Gradient Nose-Hoover Thermostat as proposed in |
summary.BNN |
Print a summary of a BNN |
tensor_embed_mat |
Embed a matrix of timeseries into a tensor |
to_bayesplot |
Convert draws array to conform with 'bayesplot' |
Truncated |
Truncates a Distribution |
vi.get_samples |
Draw samples form a variational family. |